Any general statement as to whether the secular trend of a society is eugenic or dysgenic depends upon a reliable calibration of the measurement of general intelligence. Richard Lynn set the mean IQ of the United Kingdom at 100 with a standard deviation of 15, and he calculated the mean IQs of other countries in relation to this Greenwich IQ. But because the UK
test scores could be declining, the present paper recalibrates the mean IQ 100
to the average of seven countries having a historical mean IQ of 100. By comparing Lynn-Vanhanen-IQ with PISA scores and educational attainment of native and foreign born populations transformed into the IQ metric, we confirmed brain gain and brain drain in a number of nations during recent decades. Furthermore, the growth of gross domestic product per capita can be derived as a linear function of the percentage of people with an IQ above 105 and its underlying frequency of a hypothetical major gene of intelligence.

Most
intelligence tests, such as those used by Lynn and Vanhanen (2002, 2006) in
their compilations, have been constructed in Britain
and the United
States and
have subsequently been administered to samples of the populations in other
countries throughout the world. In order to make comparisons possible, Lynn
set the mean IQ of Britain at 100 with a standard deviation of 15, and he
calcutaded the mean IQs of other countries in relation to this “Greenwich IQ”.

As
the standard of living during the phase of early industrialization
deteriorated, the height of Saxony’s
soldiers decreased within a time span of 60 years (birth years 1775-1835) by
about 6 cm (Ewert, 2006). Analogous secular trends up and down have been
reported for all industrialized countries. In the past century the acceleration
of bodily growth has been accompanied by a similar secular rise of cognitive
test scores usually called the Flynn Effect (Fernandez-Ballesteros and
Juan-Espinosa, 2001).

In
the case of the Standard Progressive Matrices test, for example, which has been
administered in many countries and which Lynn and Vanhanen (2002) used
extensively for the calculation of national IQs, the British mean IQ increased
at a rate of approximately 2 points per decade from 1938, when the test was
constructed, up to 1979. Where this and other tests have been used, adjustments
for the secular rise of IQ have been made by Lynn.
In some cases such an adjustment involved a bit of guesswork, but in retrospect
and comparing it with other data sets available now (see Tables 1 and 3), an
excellent and innovative job has been done. These adjustments can be understood
as an attempt to correct phenotypic values of IQ to its genotypic values.

Table 1

PISA scores (500; 100) of mathematical literacy and the respective means of differences from the mean of seven countries (whose Lynn-Vanhanen mean IQ is 100), these differences transformed into PISA IQ (100; 15) values, Lynn-Vanhanen estimates of IQ 2002 and 2006, and Rindermann estimates of IQ (from which 1 point is already subtracted in the lower part of the table)

Country

PISA

2000(1)

PISA

2003(2)

PISA

2006(3)

mean

diff.

PISA IQ

L/V IQ

2002(4)

L/V
IQ 2006(5)

Rind IQ

2007(6)

Belgium

520

529

520

+2

100

100

99

100-1

Canada

533

532

527

+8

101

97

99

102-1

Netherlands

(535)*

538

531

+15

102

102

100

102-1

New Zealand

537

523

522

+3

100

100

99

101-1

Sweden

510

509

502

-15

98

101

99

101-1

Switzerland

529

527

530

+6

102

101

101

101-1

UnitedKingdom

529

(512)*

495

-10

99

100

100

102-1

mean of these 7 countries

528

524

518

1,3

100,29

100,14

99,57

101,29

-1

corrected mean

527

523

517

100

100

100,29

Argentina

388

381

-138

79

96

93

88

Australia

533

524

520

+3

100

98

98

100

Austria

515

506

505

-14

98

102

100

100

Brazil

334

356

370

-169

75

87

87

83

Bulgaria

430

413

-101

85

93

93

95

Chile

384

411

-125

81

93

90

88

CzechRepublic

498

516

510

-14

98

97

98

99

Denmark

514

514

513

-9

99

98

98

98

Finland

536

544

548

+20

103

97

99

102

France

517

511

496

-14

98

98

98

99

Germany

490

503

504

-23

97

102

99

98

Greece

447

445

459

-73

89

92

92

96

Hongkong

560

550

547

+30

104

107

108

105

Hungary

488

490

491

-33

95

99

98

100

Iceland

514

515

506

-11

98

98

101

100

Indonesia

367

360

391

-150

78

89

87

85

Ireland

503

503

501

-15

98

93

92

97

Israel

433

447

-82

88

94

95

95

Italy

457

466

462

-61

91

102

102

100

Japan

557

534

523

+16

102

105

105

104

Korea (South)

547

542

547

+23

103

106

106

105

Latvia

463

483

486

-45

93

97

98

97

Luxembourg

446

493

490

-46

93

101

100

98

Mexico

387

385

406

-130

81

87

88

84

Norway

499

495

490

-27

96

98

100

99

Poland

470

490

495

-37

94

99

99

98

Portugal

454

466

466

-60

91

95

95

94

Russia

478

468

476

-45

93

96

97

98

Slovakia

-

498

492

-25

96

96

96

98

Spain

476

485

480

-42

94

97

98

97

Thailand

432

417

417

-103

85

91

91

90

Tunisia

-

359

365

-158

76

84

83

84

Turkey

-

423

424

-97

85

90

90

87

United States

493

493

474

-39

94

98

98

99

Uruguay

-

422

427

-96

86

96

96

91

(xxx)* mean of the two
other PISA values of this country

- no data

Sources:

(1) OECD (2003). Literacy skills for the world of tomorrow  further results from PISA 2000, p. 100.

(6) Rindermann, H. (2007). The g-factor of international cognitive ability comparisons: the homogeneity of results in PISA, TIMSS, PIRLS and IQ-tests. European Journal of Personality, 21, 667-706, here p. 700ff.

In
2002, after the publication of IQ and the
Wealth of Nations (Lynn and Vanhanen, 2002) and the preliminary reports of
PISA 2000, Weiss became aware that PISA tests can be understood as IQ tests
(Weiss, 2002; Lehrl, 2005) and that the transformation of PISA scores into IQ
results in very similar numbers (Weiss, 2005, 2006). PISA
scores, mean 500, standard deviation 100, can easily be transformed into IQ
values, mean 100, standard deviation 15, by adding or subtracting the deviation
from the mean in the relationship 15 : 100. For example., a PISA score of 433 corresponds to IQ 90, PISA
567 to IQ 110.

But can a PISA
score of 500 set to be an IQ of 100? The mean of 500 is the mean of all
participating countries of the OECD, not the mean of the United
Kingdom. In
the 2003 PISA study, because of the inclusion for the first time of Turkey in
the reference sample for calculating the mean of 500, the average of Germany
and other countries has risen 3 (non-transformed) PISA points (corresponding to
0.45 IQ) in comparison to 2000 without their contributing anything to such an
effect.

In 2000 the PISA
score of the United
Kingdom was
529, but in 2006 only 495. This would mean a PISA IQ of 104 in 2000 and 99 in
2006, but we had to set this 104 and 99 to be the “Greenwich IQ” of 100.
Perhaps the average IQ of the United
Kingdom was or
is actually declining, and it is not reasonable to calibrate the IQ of the
world to the waterline of a single leaking ship. In order to avoid this
possible methodological pitfall, we set the arithmetic means of the PISA scores
in 2000, 2003, and 2006 of seven countries of which the mean IQ was estimated
by Lynn and Vanhanen (2002) to be 100, as IQ 100 (see Table 1). Overall, rises
and declines of IQ within these seven countries seem to average out quite well.

As shown by Rindermann (2007),
combining the scores of the mathematics, readings and science subtests of PISA
makes no essential difference from using the mathematical subtest alone,
because all subtests of PISA
are heavily loaded with general cognitive ability. For example, in 2006 the
transformed PISA
mean of Germany
on the reading scale is IQ 98, on the science scale IQ 99, on the mathematical
scale IQ 98. Essentially, all three scales of PISA
measure general intelligence (Lehrl, 2005).

Unifying
the results of international educational research (not only of PISA
but also the analogous inclusion of TIMSS and PIRLS) and psychometric testing
(Lynn and Vanhanen, 2006) into a plausible estimate (also the intention of
Hanushek and Woessmann, 2007) of respective national IQs is the merit of
Rindermann’s contribution (2007). However, he did miss the problem of
calibration entirely. As can be seen in Table 1, the IQ values estimated by him
are on average 1 point too high.

In their publications, today’s
educational psychologists prefer to avoid the valued-laden terms “intelligence”
and “IQ” completely (Brand, 1995; Brand, Constales and Kane, 2003; Weiss,
2002). Those who dislike the term IQ can feel free to transform all IQ values
ever measured or calculated by differential psychologists (Lynn, 2008; Lynn and
Vanhanen, 2002, 2006; Weiss, 2005; Rindermann, 2007) into “competencies” and
“literacy” on 500;100 scale. In such a case all numerical relations, all
correlations and all conclusions will remain the same.

The
Effects of Selective Migration and Differential Fertility on IQ Means

Before
the author had calculated Table 2, he was convinced as anybody else that
because of different curricula in different countries educational attainments
are difficult to compare across nations. However, recent data, in which the
educational attainments of natives and migrants are reported separately, make
it possible to estimate the effects of migration on IQ means.

Educational attainments were
transformed into IQ in the following way: The OECD average of native-borns with
less than upper secondary education is 41%, which corresponds to a median
percentile of 20.5 and IQ 89. The average of people with upper secondary and
post-secondary education is 40%, which corresponds to a median percentile of 41
+ 20 = 61 and IQ 104. The average of adult people with tertiary education is 18%,
which corresponds to a median percentile of 41 + 40 + 9 = 91 and hence IQ 120.
However, because the calibrated OECD average of all OECD countries is not IQ
100 but IQ 96 (see Table 1), we have to correct IQ 89 to IQ 85, IQ 104 to IQ
100 and IQ 120 to IQ 116.

Now, in order to calculate the
mean” educational IQ” of the native-born population of Australia (see Table 2), we had to
multiply 46 x 85 = 3910, 15 x 100 = 1500, and 39 x 116 = 4524. By adding 3910 +
1500 + 4524 = 9934, divided by 100, we get a mean “educational IQ” of 99. In
the analogous way we calculate for the foreign-born population of Australia a mean IQ of 101. Because the
percentage of immigrants into Australia amounts to 25%, the mean
educational IQ of its total population is 99.

Table 2

Educational attainment of the native- and
foreign-born populations of OECD countries, percentage of immigrants, and
Educational IQ (100; 15) of the total populations

Country

Native/foreign: less than upper seconday(1)
%

- Mean IQ 85

Native/foreign: tertiary(1)

%

- Mean IQ 116

Immigrants

(2) %

Educational
IQ of total population(3)

Difference of Educ. IQ foreign/native borns(4)

Australia

46 / 38

39 / 43

25

99

+ 2

Austria

33 / 49

11 /11

12

98

- 3

Belgium

47 / 54

23 / 22

11

97

- 2

Canada

32 / 30

32 / 38

18

100

+ 2

CzechRepublic

23 / 38

10 / 13

6

99

- 2

Denmark

41 / 49

19 / 19

4

98

- 2

Finland

40 / 53

23 / 19

3

100

- 3

France

46 / 55

17 / 18

9

97

- 1

Germany

24 / 44

19 / 15

7

101

- 3

Greece

54 /45

13 / 15

1

95

+ 1

Hungary

45 / 41

11 / 20

4

96

+ 1

Ireland

48 / 30

23 / 41

11

97

+ 6

Italy

64 / 54

8 / 12

2

92

+ 2

Japan

25 / 26

27 / 30

1

102

- 2

Luxembourg

29 / 37

13 / 22

27

97

0

Mexico

72 / 37

11 / 38

1

90

+ 11

Netherlands

41 / 53

19 / 18

11

98

- 2

New Zealand

30 / 19

27 / 31

20

100

+ 2

Norway

21 / 18

23 / 31

6

102

..0

Poland

31 / 48

10 / 12

3

95

..0

Portugal

80 / 55

8 / 19

3

90

+ 5

Spain

64 / 55

19 / 22

5

94

+ 1

Sweden

25 / 30

23 / 24

12

101

- 2

Switzerland

18 / 42

26 / 24

25

102

- 3

Turkey

79 /49

5 / 17

2

90

+ 5

UnitedKingdom

29 / 41

20 / 35

12

99

0

United States

22 / 40

27 / 26

7

101

- 3

OECD average

41 / 41

18 / 23

7

96

+ 2

Sources:

(1)
OECD (2007b). OECD factbook 2007. Economic, Environmental and Social Statistics. Migration  Education - Educational attainment of immigrants. Table: The educational
attainment of the native- and foreign born populations as a percentage
of the population aged 15 and above, within each group, circa 2000. –
The upper secondary and post-secondary educational attainment (mean IQ
100) is the difference between 100 and the sum of the percentages of the
less than the upper secondary plus tertiary.

The
general correpondence of means of educational IQ, PISA IQ and Lynn-Vanhanen IQ
should not be a complete surprise. Rindermann (2007) had already found a
correlation of .78 across countries between mean IQ and educational level of
young adults, operationalized by an index composed of three measures: 1. adult
literacy rate 1991; 2. percentage of persons between 12 and 19 years old 1960 –
1985 having graduated from secondary school; and 3. the mean of years of
schooling of persons 25 years or older for 1990, 1995 and 2000.

Generally,
the differences between Lynn-Vanhanen IQ, PISA IQ and Educational IQ do not
exceed plus or minus two points. Whereas Lynn-Vanhanen IQ is based on IQ tested
samples (in some countries even on a single, small and local sample), and PISA
IQ is based on large and representative samples of schoolchildren, Educational
IQ is based on data of the total adult population. The data on educational
attainment, scaled as Educational IQ, and PISA
results confirm, in most cases within the limits of measurement error, the
results of a century of IQ testing, summarized by Lynn and Vanhanen (2002, 2006).
Whoever active in the field of educational economy had ever imagined such a
possibility?

However,
not in all countries educational attainments are well calibrated to allow
international comparisons. Obviously, the Educational IQ of the United
States does
not fit with its Lynn-Vanhanen IQ and PISA IQ. Because of the decline in the
value of a US
college education arising form increases over time in the number of persons
graduating from college, there is a stronger deflation of educational degrees
in the United States
than in other OECD countries. Also Scandinavian degrees seem be more deflated
than degrees in the Netherlands.

There
is a theory of college-going decision making (Arai, 1998) which is quite
different from human capital theory (Wößmann, 2002; Hanushek and Woesmann,
2007). This alternative theory is based on the idea that the role of higher
education is not to certify the knowledge and skills aquired in college, but
simply to convey information to society about the degree holder’s innate productivity.
According to this theory, those who have high productive capabilities acquire
higher education so that firms can identify degrees holders as more productive
and pay them more. This role of education has been called signaling, screening,
filtering or sorting by different authors (Arrow, 1973; Burdett, 1978; Spence,
2002). The holding of a degree is also seen as a signal whether an applicant is
from a rich and educated family or not. This information is used to guess their
productivity because those from rich and educated families are more productive
either on average or with certainty (Bowles and Gintis, 2002; Mulligan, 1999).
Signaling (filtering) theory assumes that higher education does not improve
students’ talents because it does not change their genotypes. An employer uses
a job applicant’s educational degree or years of education, this means in our
context his Educational IQ, to infer his position in the ability distribution.
The education system of most countries consists of primary, secondary and
tertiary education. Suppose that only primary education was initially
compulsory and that compulsory education has been extended to the secondary
level. Because of this change, the genotype of individuals who the signaling
theory considers to be of low ability and who used to receive only primary
education will now receive secondary education. Then, the average productivity
and accordingly wages of workers with secondary education will be lower than
before and will be deflated. This is because the extended years of compulsory
education will neither enhance the productivity of students nor their IQ.

Already
in the last quarter of the 19th century in England
the decrease of birth rates in the upper stratum led to the assumption of a
threat of an accompanying decrease of average giftedness. But contrary to all
such expectations cognitive test scores rose over many decades
(Fernandez-Ballesteros and Juan-Espinosa, 2001). For a geneticist (Weiss, 1992)
it seems clear that – in analogy to the already mentioned acceleration of body
height - such a rise could only be a rise in phenotypic values and not in
genotypic ones. However, under the impression of the Flynn effect, the argument
that a dysgenic development was imminent seemed to be ridiculous to the wider public
(Lynn,
1996).

In a comparative study of
national fertility surveys taken around 1970, Finland
was the only country where a positive correlation with fertility became more
accentuated as husband’s education increased (Jones, 1982). In view of the development
in other Scandinavian countries (Teasdale and Owen, 2008), it can be doubted
that such a trend has continued up to today.

A study (UN, 1995) which
analyzed nationally representative surveys of 26 countries (including 10
countries in sub-Saharan Africa, but also Egypt, Indonesia, Thailand, Brazil,
Mexico and Peru) found in all countries a strong negative correlation between
women’s years of education and their mean number of living children. This was
confirmed by Meisenberg (2008) with data from the 1990, 1995 and 2000 waves of
World Value Survey covering 78 countries. Because this correlation has been
observed for both industrialized and developing countries for more than half a
century already (Lam, 1997), economists are beginning to discuss the possible
consequences (Bishop, 1989; Goujon and Lutz, 2004). Exceptions (as temporarily Finland
was) are very few, and the elite of managers and professionals with sometimes a
relatively high number of surviving children, (causing a U-shaped distribution
of differential fertility in some countries) is numerically too small to have a
trend-changing demographic effect.

However, as it seems, within
one generation or even within a few years the impact of dysgenic fertilily on
IQ means of countries is much smaller than the impact of selective migration.

Table 3

OECD countries with at least 7 percent immigrants,
Lynn-Vanhanen estimates of IQ (2002), Educational IQ, PISA IQ, difference of
Educational IQ between foreign and native born and the percentage of immigrants
among the adult population, and difference of PISA IQ between children with
and without migrational background

Country

L/V IQ

2002(see Table 1)

Mean year of birth about 1955

Educational IQ(see Table 2)

Mean year of birth about 1960

PISA IQ(see Table 1)

Mean year of birth 1988

Difference.
of Educ. IQ foreign/native born and percentage of immigrants(see Table 2)

Difference of PISA IQ between children with and
without migrational background(1)

On
average, shoolchildren tested by PISA
are born about one generation later than the subjects of IQ test samples
summarized by Lynn and Vanhanen (2002). By comparing Lynn-Vanhanen IQ means
with PISA IQ means, we see clearly that in Ireland,
Australia,
Canada
and New Zealand
where the Educational IQ of immigrants is higher than of natives the IQ of the
total population is rising, and in Germany
and Austria
where the Educational IQ of immigrants is lower than that of natives, the IQ is
declining. A rise of 5 IQ points in Ireland
is contrasted with a 5-points decline in Germany.
Background data from many sources (List and Schnabel, 2004; Abelshauser, 2008;
Belot and Hatton, 2008; Brücker and Ringer, 2008; Heinsohn, 2008; Zimmermann,
2005) corroborate that this rise and fall is a real one.

7%
of immigrants in Germany
(see Table 3) are clearly an underestimate (immigrants from the former Soviet
Union obtain in most cases the German
citizenship and are not counted as foreigners in governmental statistics).
Other sources (OECD, 2004) speak of more than 20% of schoolchildren with at
least one foreign born parent (the microcensus of 2007 has counted 27% of such
families; Brücker and Ringer, 2008.) In countries in which the the fertility of
immigrants is higher than the fertility of the natives, the proportion of those
with a migration background is predictably often far higher among school-aged
children than in the total population.

Taken
OECD countries as a whole the mean IQ of immigrants is 2 points higher than of
native borns (OECD, 2007b), overriding in such a way the effects of dysgenic
fertility in many industrialized countries. The brain gain, especially of
English-speaking countries, is contrasted with a brain drain from third-world
and East European countries (Docquier, 2006; Docquier and Marfuk, 2006; Von der
Oelsnitz et al. 2007; Belot and Hatton, 2008). The combination of brain drain
with dysgenic fertility is leading to a fast decline of mean IQ, especially in South
Africa and
some countries of Latin America
(Weiss, 2007; Lynn, 2008).

Clearly,
within one generation the effect of selective migration on IQ (of course,
including the fertility of the migrants) means is in many countries much higher
than the effect of differential fertility among the natives. The data show that
Finland
seems to be the only country in the world where eugenic fertiltiy has
contributed to a rise in mean IQ during the last half century.

The
Law of the Vital Few

Another
possibility for measuring differences between countries would be in terms of
allele frequencies of genes underlying the IQ distribution. Until now, attempts
to discover effects of hypothetical major genes underlying this distribution
(arguments in favor and against such a hypothesis are reviewed in Weiss, 1992,
2000) have been fruitless (Payton, 2006). In the present state of knowledge, this does neither prove
nor disprove the existence or non-existence of such genes. In
most countries, funding of such research and even the hypothesis of such genes
is politically incorrect (Weiss, 2007).

Francis
Galton was the first to replace mere speculation on the inheritance of talent
with statistical data (Galton, 1869). Weiss himself needed decades to see the
parallels between the data collected by him in 1970/71 and his follow-up in
1993 (see Weiss, 1992; 1994) and the earlier data of Galton, Terman and
coworkers (Oden, 1968), and Brimhall (1922/23). When preparing an overhead
transparency for a lecture, he wrote out the data in parallel (Weiss, 2000, p.
80; see Table 4) and was impressed.

Table 4

Highly gifted men and the percentage of their
highly gifted male relatives

(classified by occupation and achievement in the
studies by Galton, Terman, Brimhall, and Weiss)

Brimhall, D. R. (1922/23).
Family resemblances among American men of science.The American Naturalist, 56,
504-547, 57, 74-88, 137-152,
and 326-344.In 1915 questionnaires were filled in by 956 distinguished American men of science and their relatives.

Who had ever imagined such a similarity to be possible?
On the one hand (Galton, 1869) we see famous scientists of past centuries and
their famous relatives belonging to the upper stratum of society, on the other
side schoolchildren of a so-called socialist state (Weiss, 1992, 1994),
selected by nation-wide mathematical competitions, and their relatives
scattered over the whole spectrum of jobs and occupations. In addition, we see
the Californian top performers of Terman’s Stanford-Binet IQ testing in 1916
(Oden, 1968), and Brimhall’s sample (1922/23) of famous American men of
science. Such a parallel can only arise, if Galton’s and Brimhall’s criteria of
fame, Weiss’ criterion of giftedness for mathematics, science and high accomplishments
in other fields, and Terman’s definition of high IQ have something substantial
in common. Unconsciously (or perhaps better: very consciously), Galton, Terman,
Brimhall, and Weiss must have shared the same intuitive understanding of a
qualitative threshold beyond which we can speak of well-above-average cognitive
ability. Any contribution to the genetics of general cognitive ability has to
try to find an explanation for the regularity of this table.

If all individuals within a population share the same
gene, as for example all humans share a gene to develop four limbs, then 100
percent of relatives of all degrees (in Table 4) have four limbs. However, if
there is a genetic polymorphism and one allele is very rare and its frequency
in the overall population near zero, only homozygote carriers of such a rare
allele may exhibit the very rare characteristic, for example, to be an albino.
In this case, the frequency of albinism among relatives decreases very rapidly
with each degree of decreasing consanguinity to the proband. In other words,
from the slope of the decrease the allele frequency underlying the character
can be estimated. By applying the method of stochastic Mendelian matrices
(Geppert and Koller, 1938; Li and Sacks, 1954) as early as 1971 Weiss (see
Weiss, 1992, 2000) put forward the hypothesis that a major gene, M1, could
explain the frequency of fame and high giftedness among the consanguine kin of
the highly gifted. In a population with a mean IQ of about 100, the frequency q of this hypothetical allele M1 was
estimated to be about 0.20. Of course, the distribution of Table 4 is predicted
not only by a major gene hypothesis, but can also be modeled by polygenic
inheritance with small effects of many loci. However, Occams’ razor states that
the explanation of any phenomenon should make as few assumptions as possible.

The hypothesis of a major gene locus of IQ was and is not
based on Table 4 alone, but of a large body of data showing Mendelian
segregation of giftedness and IQ within families (Weiss, 1992, 1994), from
which the conclusion was drawn that individuals with a genotypical IQ above 123
are homozygote M1M1, and those above 104 are heterozygote M1M2 (where M2 stands
for an unknown series of alleles). As an addition, the hypothetical gene frequency
of M1 was inferred from Table 4.

According to Jensen (1980, p.
115.): “The socially and personally most important threshhold regions on the IQ
scale are those that differentiate with high probability between persons who
because of their level of general mental ability ... can or cannot succeed in
the academic or college preparatory curriculum through high school (about
105).” Independently, an author who is publishing on the world wide web under
the pseudonym “La Griffe du Lion” [The Paw of the Lion] put forward his “smart
fraction theory” (2004), stating: “In market economies, per capita GDP is
directly proportional to the population fraction with IQ greater than 105. …According to the 1992
Wonderlic Personnel Test and
Scholastic Level Exam Users Manual, at an IQ level of 106 we might
expect to find bookkeepers, credit clerks, laboratory technicians, salesmen and
secretaries. At slightly higher IQs we find registered nurses, sales account
executives, administrative assistants and store managers. These people are not
rocket scientists. They are, however, vital to a flourishing economy. Any
nation … needs a cognitive core to carry its water.” While Lynn and Vanhanen
(2002) found a nonlinear relationship
between GDP and IQ, La Griffe du Lion asserts that per capita GDP is related
linearly to the percentage of “smart fraction”, but his figures are not fully
convincing. However, we can go a step further and extend his argument.

From the Hardy-Weinberg-law of
population genetics follows that the frequency q of the hypothetical major gene M1 is (1-q)2 + 2q(1-q) + q2
= 1. From q = 0.20 follows that
2q(1-q) + q2 =
0.36. This frequency of 0.36 and its percentile rank not only corresponds to an
IQ of 105 (in a population with a mean IQ of 100), but is also identical with
the smart fraction suggested by La Griffe du Lion. By extracting the root from
the non-smart fraction (1-q)2,
we get the allele frequency q of M1
(see Table 5) in different countries.

Applying a rule of thumb, we
see that this frequency multiplied by about 1000 gives the theoretical GDP in
1998 (La Griffe du Lion, 2004). Now, indeed, the relationship between GDP and
the frequency of a major gene underlying above-average IQ is a linear one. Also
Docquier (2006) found a linear correlation of .68 between GDP per capita
(average 1995-2005) and the proportion of worker with tertiary education in the
labor force. This proportion is not identical with the smart fraction, but about
the half of it.

The Pareto principle (also
known as the 80-20 rule), the law of the vital few, states that for many events
80% of the effects come from 20% of the causes (Koch, 2000). The power of a
nation does not depend of its mere number, but of the percentage of its
cognitive elite, optimized by social evolution (Weiss, 2000). Highly
intelligent people are networking, and the economic effect of networking is the
square of the nodes of the network, i.e. in our case the square of the number
of people involved (Koch, 2000). Korotayev, Malkov & Khaltourina (2006)
found out that the dynamics of hyperbolic growth of world GDP can be described
by a simple equation containing one quadratic term. We see no other solution
than for this term to be identical with q2,
where the root of q2 predicts for a population the
percentage of highly gifted with an IQ above 123.

Advanced
methods, applied very successfully in the low IQ range, for example, homozygosity mapping and
comparative genomic hybridization within consangineous families (Knight et al.,
2008), have still never been used in order to detect high IQ genes. Of course,
the IQ is influenced by hundred of minor genes and other effects, measurable
under certain circumstances and in subpopulations, but this does not exclude
the possibility of major gene effects.

A
rationale for the search of such genes should include the following knowledge
and steps: 1. homozygosity mapping among the relatives of highly gifted, who
have an IQ above 123 (for them their Educational IQ could be a good estimate);
2. nonsynomously coding SNPs and other genetic polymorphisms which are found to
be homozygous among relatives should be checked whether their distribution of
allele frequencies in the data bases does fit the expected racial distribution:
about 0.20 of the rare allele among Eurasian populations and approximating 0.00
in Subsaharan Africa; 3. after 1 and 2. are given, the possible association
between genotypus and IQ should be investigated in a larger sample.

Indeed,
among hundredthousands of SNPs already investigated there are very, very few
which fit the second criterium, for example: rs1186902 of the neurotransmitter
gene GABRR1, which exhibits a wide range of mRNA expression (Khaitovich et al.,
2008); similar rs4667001 of the gene ZNF804A, the only gene to be confirmed
associated with schizophrenia in a genome wide association study (O’Donovan et
al., 2008). - Because a SNP of the gene NQO2 fulfilled the condition of step 2,
the investigation suggested by V. Weiss in January 2006 led to the discovery of
a minor gene of IQ (Payton et. al., 2008).

In 1998, the GDP per capita of
a country with a hypothetical gene frequency of 0.20 for M1 was about nine-fold
higher than for a country with a frequency of 0.02; the absolute difference in
GDP per capita was about $19.000. Perhaps in 2030 or 2040 the GDP all over the
world will be four times higher than in 1998. In this case the per capita
difference between countries with gene frequencies of 0.20 and 0.02 will be
about $70.000. This widening of the gap between highly industrialized and
underdeveloped countries has been taking place since the beginning of the
industrial revolution (Clark, 2007; Galor and Moav, 2002).

The expected decline in the world’s average genotypical
IQ from 95 in 1950 to 87 in 2050 (Weiss, 2007; similar Lynn and Harvey, 2008)
would mean a decrease of the hypothetical gene frequency q of Ml from 0.12 to 0.05 and a decrease of the smart fraction
(with an IQ above 105) from 22% to about 10%; that means a decrease of about 4%
per generation of the latter.

It is common knowledge that
non-market economies have lower growth rates of per-capita GDP than the market
economies. While some former non-market countries with a high average IQ such
as Estonia,
the Czech Republic,
Slovenia
and especially China
are narrowing the gap, those with a low average IQ seem to have no chance to
catch up. But even among market economies we haveat one extreme the impressive
success story of Singapore
(Lee, 2000) and on the other extreme countries such as Haiti
and Zimbabwe
which are not only backward, but suffer from mismanagement and brain drain. In 1968, the Pacific island of Nauru possessed the highest GDP per capita in the world (cited
from Wikipedia, 2008) due to its rich phosphate deposits. Today, after the
exhaustion of these deposits, Nauru - faced with chaos amid political strife
and the collapse of the economy caused by mismanagement and corruption - has a
GDP more in accordance with the gene frequency of M1 in its population. One of
the criteria which differentiate science from speculation is the power of
prediction. In 2007, oil-producing Equatorial Guinea, a country with an average
IQ of 59 (Lynn and Vanhanen, 2002), one of the lowest in the world, had a GDP
per capita of $44,100 (CIA, 2008), one of the highest in the world. We predict
that, after the exhaustion of the oil, the GDP of this country will fall back
into a range typical for a country with a hypothetical gene frequency of M1
below 0.02. As long as the oil is flowing, a number of specialists and dealers
of Lebanese, Chinese, Indian and other origins make money, but they will
abandon such a country after the boom.

In Table 5
we included only countries with a long history of market economy and with at
least two sets of data from PISA studies. Therefore, the overall impression of
this table is misleading, because three quarters of world population have a GDP
per capita below Brazil. In 2007, in Subsaharan Africa there are 12 countries
with a GDP below $1000, all with a hypothetical gene frequency of M1 below
0.02. Until now, there seems to be no Flynn Effect of importance in these
countries. Therefore, a truncation of national IQs to a minimum of 80 would
make sense, but would not make much difference in terms of gene frequencies.

Some countries (for example,
Brazil, Israel, South Africa) with a socially and regionally segregated or
highly stratified population (and a high Gini-index) have a much higher GDP
than can be expected from their average IQ. In such countries, the variance of
IQ of the overall population is higher than 15, and we should strive to replace
the theoretical frequency of M1 by its real frequency. Most suitable would be
measuring the percentage of subjects with an IQ above 105 directly, another
possibility calculating the smart fraction not from the idealized bell curve
with variance 15, but from the measured variance. So far, we do not have
sufficient data to make such an improvement.

Even within developed nations the
difference between prosperous and more backward regions amounts to 10 and more
IQ points. For example, in Germany the IQ average of Bavaria is about 10 points
higher than that of Bremen (Ebenrett, Hansen and Puzicha, 2003); in Italy the
difference between Venice and Sicily is 13 points; in Spain the difference
between Aragon and Andalusia 8 points (OECD, 2007a); and in the United States
the difference between New Hampshire and Mississipi is 10 points. Such
differences, aggravated by internal migration between the economic core and the
backward regions (Ebenrett, Hansen and Puzicha, 2003) - but not always of such
magnitude - will be found in any country. However, the average IQ of 102 for
Italy (Lynn and Vanhanen, 2002) as a whole was never correct and can have been
obtained only from testing in that country’s northern regions.

When merging the 16 German states
into one league table with the other 28 OECD countries that report PISA 2003
performance data, Bavaria (IQ 102) takes 5th place internationally,
while the largest state of Germany, North Rhine-Westphalia (IQ 94) takes 35th
place and the city state of Bremen 39th place (IQ 92) out of the 44
countries and states (Wößmann, 2007).

There should be
doubt whether the average IQ of China is really as high as 105 (Lynn and
Vanhanen, 2006) or even 106 (Rindermann, 2007; minus 1 already subtracted).
This average may hold for the coastal regions, but perhaps not for the
provinces in the interior (neighboring Kyrgyzystan, for example, has in 2006 a
PISA IQ of only 69). In view of the fact that also within industrialized
countries (see further below) the difference between the prosperous and the
economically backward regions amounts to about 10 IQ points, why should the
situation in China be different?
Also, for decades in China, as in most countries all over the world, highly
qualified women bear only half the number of children as unqualified women:
those with primary education 2.14 children, and those with tertiary education
1.08 (Goujon and Lutz, 2004). It is simply naiveté to believe a
one-child-policy has changed anything in this respect.

Within Brazil,
the federal states of the South have an average IQ and GDP per capita similar
to South Europe
and four times higher than the states in the north-east of Brazil
(CIA, 2007). For such differences to arise, natural selection - from the Stone
Age up to the present (Lynn,
1996) - is not the only and most likely explanation (Weiss, 1991). Political
turmoil and ethnic cleansing can eliminate or drive away the gifted of a
country, and within a very short time harm the economy for decades to come
(Weiss, 2000). Highly-skilled citizens from stagnating economies are unlikely
to merely watch their standard of living decline, and they will vote with their
feet. Their migration amplifies economic divergences within and between
countries. Since January 2007, as Romania
was admitted to the European Union, about 1 million of former inhabitants of Romania
(among them many gypsies) emigrated to Italy
and Spain.
Within the last four years more than 800,000 East Europeans got a work permit
in the United Kingdom,
and alone within the last two years 1.5 million emigrated from Poland
(Sinn, 2008). Up to now there are no statistical data availabe to account for
the effects of this most recent migration on the IQ means of the respective countries.

There are three types of men
(Weiss and Weiss, 2003): Men (with IQ above 123), who invent machines, men
(with IQ above 104), who repair machines, and men, who use machines. In a
country where there are not enough men to construct and to repair a bridge,
sooner or later traffic by railroad will break down (Malloy, 2008). Now, for
example, Angola
needs Chinese engineers to repair railroads and bridges.

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Hanushek, E. A., and Woessmann,
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